Overview

Dataset statistics

Number of variables22
Number of observations40309
Missing cells24170
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory141.3 B

Variable types

Text5
Numeric8
Categorical9

Alerts

modelo has a high cardinality: 939 distinct valuesHigh cardinality
tipo_de_combustible is highly imbalanced (70.5%)Imbalance
motor has 3008 (7.5%) missing valuesMissing
cilindrada has 8992 (22.3%) missing valuesMissing
potencia has 8735 (21.7%) missing valuesMissing
control_de_traccion has 3433 (8.5%) missing valuesMissing
kilometros is highly skewed (γ1 = 30.08883515)Skewed
id_car has unique valuesUnique
link has unique valuesUnique
age has 747 (1.9%) zerosZeros

Reproduction

Analysis started2024-05-20 05:23:29.137457
Analysis finished2024-05-20 05:23:38.270951
Duration9.13 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id_car
Text

UNIQUE 

Distinct40309
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:38.653615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters524017
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40309 ?
Unique (%)100.0%

Sample

1st rowMCO2391186016
2nd rowMCO2391197332
3rd rowMCO1426750375
4th rowMCO1426711773
5th rowMCO1425593285
ValueCountFrequency (%)
mco2391186016 1
 
< 0.1%
mco2364656310 1
 
< 0.1%
mco1426750375 1
 
< 0.1%
mco1426711773 1
 
< 0.1%
mco1425593285 1
 
< 0.1%
mco2387326852 1
 
< 0.1%
mco1424639637 1
 
< 0.1%
mco1423454057 1
 
< 0.1%
mco2375421964 1
 
< 0.1%
mco1424598805 1
 
< 0.1%
Other values (40299) 40299
> 99.9%
2024-05-20T00:23:39.167526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 61047
11.6%
1 56908
10.9%
4 48709
9.3%
3 47908
9.1%
M 40309
7.7%
C 40309
7.7%
O 40309
7.7%
6 33743
 
6.4%
5 32966
 
6.3%
0 30992
 
5.9%
Other values (3) 90817
17.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 524017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 61047
11.6%
1 56908
10.9%
4 48709
9.3%
3 47908
9.1%
M 40309
7.7%
C 40309
7.7%
O 40309
7.7%
6 33743
 
6.4%
5 32966
 
6.3%
0 30992
 
5.9%
Other values (3) 90817
17.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 524017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 61047
11.6%
1 56908
10.9%
4 48709
9.3%
3 47908
9.1%
M 40309
7.7%
C 40309
7.7%
O 40309
7.7%
6 33743
 
6.4%
5 32966
 
6.3%
0 30992
 
5.9%
Other values (3) 90817
17.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 524017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 61047
11.6%
1 56908
10.9%
4 48709
9.3%
3 47908
9.1%
M 40309
7.7%
C 40309
7.7%
O 40309
7.7%
6 33743
 
6.4%
5 32966
 
6.3%
0 30992
 
5.9%
Other values (3) 90817
17.3%

title
Text

Distinct25469
Distinct (%)63.2%
Missing0
Missing (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:39.883614image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length60
Median length48
Mean length29.909648
Min length3

Characters and Unicode

Total characters1205628
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20846 ?
Unique (%)51.7%

Sample

1st rowJeep Compass 2.4 Limited
2nd rowMazda 2 1.5 2010
3rd row Mazda 3 Lxha7 At 2.0 2010
4th rowRenault Kangoo F76 1.6 2010
5th rowHyundai Tucson
ValueCountFrequency (%)
2.0 5111
 
2.4%
chevrolet 5009
 
2.4%
renault 4848
 
2.3%
1.6 4603
 
2.2%
toyota 3967
 
1.9%
mazda 3699
 
1.7%
ford 3466
 
1.6%
nissan 3018
 
1.4%
kia 2642
 
1.2%
4x4 2608
 
1.2%
Other values (4969) 173257
81.6%
2024-05-20T00:23:40.616894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179467
 
14.9%
e 72555
 
6.0%
a 65062
 
5.4%
o 59468
 
4.9%
2 55096
 
4.6%
i 51317
 
4.3%
t 50213
 
4.2%
r 49522
 
4.1%
0 47203
 
3.9%
n 45184
 
3.7%
Other values (93) 530541
44.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1205628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
179467
 
14.9%
e 72555
 
6.0%
a 65062
 
5.4%
o 59468
 
4.9%
2 55096
 
4.6%
i 51317
 
4.3%
t 50213
 
4.2%
r 49522
 
4.1%
0 47203
 
3.9%
n 45184
 
3.7%
Other values (93) 530541
44.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1205628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
179467
 
14.9%
e 72555
 
6.0%
a 65062
 
5.4%
o 59468
 
4.9%
2 55096
 
4.6%
i 51317
 
4.3%
t 50213
 
4.2%
r 49522
 
4.1%
0 47203
 
3.9%
n 45184
 
3.7%
Other values (93) 530541
44.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1205628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
179467
 
14.9%
e 72555
 
6.0%
a 65062
 
5.4%
o 59468
 
4.9%
2 55096
 
4.6%
i 51317
 
4.3%
t 50213
 
4.2%
r 49522
 
4.1%
0 47203
 
3.9%
n 45184
 
3.7%
Other values (93) 530541
44.0%

link
Text

UNIQUE 

Distinct40309
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:40.961025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length113
Median length101
Mean length82.000248
Min length56

Characters and Unicode

Total characters3305348
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40309 ?
Unique (%)100.0%

Sample

1st rowhttps://carro.mercadolibre.com.co/MCO-2391186016-jeep-compass-24-limited-_JM
2nd rowhttps://carro.mercadolibre.com.co/MCO-2391197332-mazda-2-15-2010-_JM
3rd rowhttps://carro.mercadolibre.com.co/MCO-1426750375-mazda-3-lxha7-at-20-2010-_JM
4th rowhttps://carro.mercadolibre.com.co/MCO-1426711773-renault-kangoo-f76-16-2010-_JM
5th rowhttps://carro.mercadolibre.com.co/MCO-1425593285-hyundai-tucson-_JM
ValueCountFrequency (%)
https://carro.mercadolibre.com.co/mco-2391186016-jeep-compass-24-limited-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-2364656310-mitsubishi-lancer-20-avanzado-2010-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-1426750375-mazda-3-lxha7-at-20-2010-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-1426711773-renault-kangoo-f76-16-2010-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-1425593285-hyundai-tucson-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-2387326852-renault-stepway-16l-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-1424639637-chevrolet-spark-lt-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-1423454057-chevrolet-aveo-emotion-16-gt-2010-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-2375421964-kia-sportage-_jm 1
 
< 0.1%
https://carro.mercadolibre.com.co/mco-1424598805-renault-sandero-16-mecanico-_jm 1
 
< 0.1%
Other values (40299) 40299
> 99.9%
2024-05-20T00:23:41.416487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 295895
 
9.0%
o 221966
 
6.7%
r 217981
 
6.6%
c 197299
 
6.0%
e 159062
 
4.8%
a 158991
 
4.8%
t 145069
 
4.4%
. 120927
 
3.7%
/ 120927
 
3.7%
2 116143
 
3.5%
Other values (35) 1551088
46.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3305348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 295895
 
9.0%
o 221966
 
6.7%
r 217981
 
6.6%
c 197299
 
6.0%
e 159062
 
4.8%
a 158991
 
4.8%
t 145069
 
4.4%
. 120927
 
3.7%
/ 120927
 
3.7%
2 116143
 
3.5%
Other values (35) 1551088
46.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3305348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 295895
 
9.0%
o 221966
 
6.7%
r 217981
 
6.6%
c 197299
 
6.0%
e 159062
 
4.8%
a 158991
 
4.8%
t 145069
 
4.4%
. 120927
 
3.7%
/ 120927
 
3.7%
2 116143
 
3.5%
Other values (35) 1551088
46.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3305348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 295895
 
9.0%
o 221966
 
6.7%
r 217981
 
6.6%
c 197299
 
6.0%
e 159062
 
4.8%
a 158991
 
4.8%
t 145069
 
4.4%
. 120927
 
3.7%
/ 120927
 
3.7%
2 116143
 
3.5%
Other values (35) 1551088
46.9%

price
Real number (ℝ)

Distinct2380
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90219373
Minimum1800000
Maximum2.48 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:41.534283image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1800000
5-th percentile28000000
Q144000000
median68000000
Q31.1 × 108
95-th percentile2.27 × 108
Maximum2.48 × 109
Range2.4782 × 109
Interquartile range (IQR)66000000

Descriptive statistics

Standard deviation75456945
Coefficient of variation (CV)0.83637186
Kurtosis46.786003
Mean90219373
Median Absolute Deviation (MAD)28100000
Skewness4.1230501
Sum3.6366527 × 1012
Variance5.6937505 × 1015
MonotonicityNot monotonic
2024-05-20T00:23:41.686189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45000000 350
 
0.9%
65000000 325
 
0.8%
55000000 318
 
0.8%
40000000 311
 
0.8%
35000000 296
 
0.7%
42000000 291
 
0.7%
60000000 290
 
0.7%
75000000 287
 
0.7%
38000000 282
 
0.7%
85000000 269
 
0.7%
Other values (2370) 37290
92.5%
ValueCountFrequency (%)
1800000 2
< 0.1%
2050000 1
< 0.1%
2100000 1
< 0.1%
3000000 1
< 0.1%
3080000 1
< 0.1%
3750000 1
< 0.1%
3800000 1
< 0.1%
4300000 1
< 0.1%
4900000 1
< 0.1%
5400000 1
< 0.1%
ValueCountFrequency (%)
2480000000 1
< 0.1%
1290000000 1
< 0.1%
1260000000 1
< 0.1%
1259000000 1
< 0.1%
1190000000 1
< 0.1%
1090000000 1
< 0.1%
1080000000 1
< 0.1%
1030000000 2
< 0.1%
1020000000 1
< 0.1%
999000000 1
< 0.1%
Distinct18947
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:42.062665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length30
Median length25
Mean length14.892605
Min length3

Characters and Unicode

Total characters600306
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17384 ?
Unique (%)43.1%

Sample

1st rowTUC_LAPALMERACHIA
2nd rowZHERRERA84044
3rd rowOGARZON81312
4th rowJARROYAVE29599
5th rowTUC_SUBA
ValueCountFrequency (%)
autos 865
 
1.9%
usados 569
 
1.2%
sas 534
 
1.1%
s.a.s 397
 
0.9%
tuc_calle134 361
 
0.8%
tuc_paloquemao 298
 
0.6%
tuc_niza2 281
 
0.6%
concesionario 243
 
0.5%
tuc_cvcasatorousadosav68 230
 
0.5%
rrautosdiego 172
 
0.4%
Other values (19342) 42720
91.5%
2024-05-20T00:23:42.599187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 69812
 
11.6%
O 47656
 
7.9%
E 36453
 
6.1%
S 36335
 
6.1%
R 35427
 
5.9%
I 29234
 
4.9%
U 28590
 
4.8%
C 26497
 
4.4%
T 25789
 
4.3%
L 24905
 
4.1%
Other values (43) 239608
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 600306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 69812
 
11.6%
O 47656
 
7.9%
E 36453
 
6.1%
S 36335
 
6.1%
R 35427
 
5.9%
I 29234
 
4.9%
U 28590
 
4.8%
C 26497
 
4.4%
T 25789
 
4.3%
L 24905
 
4.1%
Other values (43) 239608
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 600306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 69812
 
11.6%
O 47656
 
7.9%
E 36453
 
6.1%
S 36335
 
6.1%
R 35427
 
5.9%
I 29234
 
4.9%
U 28590
 
4.8%
C 26497
 
4.4%
T 25789
 
4.3%
L 24905
 
4.1%
Other values (43) 239608
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 600306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 69812
 
11.6%
O 47656
 
7.9%
E 36453
 
6.1%
S 36335
 
6.1%
R 35427
 
5.9%
I 29234
 
4.9%
U 28590
 
4.8%
C 26497
 
4.4%
T 25789
 
4.3%
L 24905
 
4.1%
Other values (43) 239608
39.9%

city
Categorical

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size355.5 KiB
bogota dc
16138 
antioquia
11084 
valle del cauca
3549 
cundinamarca
1670 
atlantico
 
1453
Other values (21)
6415 

Length

Max length18
Median length9
Mean length9.5138307
Min length4

Characters and Unicode

Total characters383493
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbogota dc
2nd rowbogota dc
3rd rowbogota dc
4th rowbogota dc
5th rowbogota dc

Common Values

ValueCountFrequency (%)
bogota dc 16138
40.0%
antioquia 11084
27.5%
valle del cauca 3549
 
8.8%
cundinamarca 1670
 
4.1%
atlantico 1453
 
3.6%
santander 1302
 
3.2%
risaralda 1096
 
2.7%
caldas 548
 
1.4%
norte de santander 527
 
1.3%
boyaca 509
 
1.3%
Other values (16) 2433
 
6.0%

Length

2024-05-20T00:23:42.732859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bogota 16138
25.0%
dc 16138
25.0%
antioquia 11084
17.2%
cauca 3661
 
5.7%
valle 3549
 
5.5%
del 3549
 
5.5%
santander 1829
 
2.8%
cundinamarca 1670
 
2.6%
atlantico 1453
 
2.2%
risaralda 1096
 
1.7%
Other values (19) 4432
 
6.9%

Most occurring characters

ValueCountFrequency (%)
a 68633
17.9%
o 47287
12.3%
t 33266
8.7%
c 29682
7.7%
i 28116
 
7.3%
d 25879
 
6.7%
24290
 
6.3%
n 20732
 
5.4%
b 17099
 
4.5%
u 17007
 
4.4%
Other values (13) 71502
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 383493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 68633
17.9%
o 47287
12.3%
t 33266
8.7%
c 29682
7.7%
i 28116
 
7.3%
d 25879
 
6.7%
24290
 
6.3%
n 20732
 
5.4%
b 17099
 
4.5%
u 17007
 
4.4%
Other values (13) 71502
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 383493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 68633
17.9%
o 47287
12.3%
t 33266
8.7%
c 29682
7.7%
i 28116
 
7.3%
d 25879
 
6.7%
24290
 
6.3%
n 20732
 
5.4%
b 17099
 
4.5%
u 17007
 
4.4%
Other values (13) 71502
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 383493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 68633
17.9%
o 47287
12.3%
t 33266
8.7%
c 29682
7.7%
i 28116
 
7.3%
d 25879
 
6.7%
24290
 
6.3%
n 20732
 
5.4%
b 17099
 
4.5%
u 17007
 
4.4%
Other values (13) 71502
18.6%

color
Categorical

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size629.8 KiB
otro
15381 
gris
7162 
blanco
6306 
plateado
5088 
rojo
2318 
Other values (8)
4054 

Length

Max length8
Median length4
Mean length4.9002456
Min length4

Characters and Unicode

Total characters197524
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownegro
2nd rowblanco
3rd rowplateado
4th rowblanco
5th rowplateado

Common Values

ValueCountFrequency (%)
otro 15381
38.2%
gris 7162
17.8%
blanco 6306
15.6%
plateado 5088
 
12.6%
rojo 2318
 
5.8%
negro 2013
 
5.0%
azul 1420
 
3.5%
dorado 200
 
0.5%
marron 162
 
0.4%
verde 124
 
0.3%
Other values (3) 135
 
0.3%

Length

2024-05-20T00:23:42.881674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
otro 15381
38.2%
gris 7162
17.8%
blanco 6306
15.6%
plateado 5088
 
12.6%
rojo 2318
 
5.8%
negro 2013
 
5.0%
azul 1420
 
3.5%
dorado 200
 
0.5%
marron 162
 
0.4%
verde 124
 
0.3%
Other values (3) 135
 
0.3%

Most occurring characters

ValueCountFrequency (%)
o 49440
25.0%
r 27642
14.0%
t 20484
10.4%
a 18581
 
9.4%
l 12945
 
6.6%
g 9175
 
4.6%
n 8605
 
4.4%
e 7364
 
3.7%
i 7235
 
3.7%
s 7162
 
3.6%
Other values (9) 28891
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 197524
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 49440
25.0%
r 27642
14.0%
t 20484
10.4%
a 18581
 
9.4%
l 12945
 
6.6%
g 9175
 
4.6%
n 8605
 
4.4%
e 7364
 
3.7%
i 7235
 
3.7%
s 7162
 
3.6%
Other values (9) 28891
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 197524
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 49440
25.0%
r 27642
14.0%
t 20484
10.4%
a 18581
 
9.4%
l 12945
 
6.6%
g 9175
 
4.6%
n 8605
 
4.4%
e 7364
 
3.7%
i 7235
 
3.7%
s 7162
 
3.6%
Other values (9) 28891
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 197524
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 49440
25.0%
r 27642
14.0%
t 20484
10.4%
a 18581
 
9.4%
l 12945
 
6.6%
g 9175
 
4.6%
n 8605
 
4.4%
e 7364
 
3.7%
i 7235
 
3.7%
s 7162
 
3.6%
Other values (9) 28891
14.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size354.4 KiB
si
30711 
no
9598 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters80618
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsi
2nd rowsi
3rd rowsi
4th rowsi
5th rowsi

Common Values

ValueCountFrequency (%)
si 30711
76.2%
no 9598
 
23.8%

Length

2024-05-20T00:23:42.982501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:23:43.135090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
si 30711
76.2%
no 9598
 
23.8%

Most occurring characters

ValueCountFrequency (%)
s 30711
38.1%
i 30711
38.1%
n 9598
 
11.9%
o 9598
 
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80618
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 30711
38.1%
i 30711
38.1%
n 9598
 
11.9%
o 9598
 
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80618
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 30711
38.1%
i 30711
38.1%
n 9598
 
11.9%
o 9598
 
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80618
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 30711
38.1%
i 30711
38.1%
n 9598
 
11.9%
o 9598
 
11.9%

marca
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size398.9 KiB
chevrolet
5114 
renault
5023 
toyota
3996 
mazda
3694 
ford
3465 
Other values (15)
19017 

Length

Max length13
Median length7
Mean length6.4364534
Min length3

Characters and Unicode

Total characters259447
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowjeep
2nd rowmazda
3rd rowmazda
4th rowrenault
5th rowhyundai

Common Values

ValueCountFrequency (%)
chevrolet 5114
12.7%
renault 5023
12.5%
toyota 3996
9.9%
mazda 3694
9.2%
ford 3465
8.6%
nissan 3028
7.5%
kia 2638
6.5%
volkswagen 2514
 
6.2%
bmw 2206
 
5.5%
mercedes_benz 2116
 
5.2%
Other values (10) 6515
16.2%

Length

2024-05-20T00:23:43.234863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chevrolet 5114
12.7%
renault 5023
12.5%
toyota 3996
9.9%
mazda 3694
9.2%
ford 3465
8.6%
nissan 3028
7.5%
kia 2638
6.5%
volkswagen 2514
 
6.2%
bmw 2206
 
5.5%
mercedes_benz 2116
 
5.2%
Other values (10) 6515
16.2%

Most occurring characters

ValueCountFrequency (%)
e 28698
 
11.1%
a 27289
 
10.5%
o 21384
 
8.2%
t 18831
 
7.3%
n 17781
 
6.9%
r 15718
 
6.1%
l 13051
 
5.0%
d 13047
 
5.0%
s 12632
 
4.9%
i 10710
 
4.1%
Other values (15) 80306
31.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 259447
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 28698
 
11.1%
a 27289
 
10.5%
o 21384
 
8.2%
t 18831
 
7.3%
n 17781
 
6.9%
r 15718
 
6.1%
l 13051
 
5.0%
d 13047
 
5.0%
s 12632
 
4.9%
i 10710
 
4.1%
Other values (15) 80306
31.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 259447
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 28698
 
11.1%
a 27289
 
10.5%
o 21384
 
8.2%
t 18831
 
7.3%
n 17781
 
6.9%
r 15718
 
6.1%
l 13051
 
5.0%
d 13047
 
5.0%
s 12632
 
4.9%
i 10710
 
4.1%
Other values (15) 80306
31.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 259447
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 28698
 
11.1%
a 27289
 
10.5%
o 21384
 
8.2%
t 18831
 
7.3%
n 17781
 
6.9%
r 15718
 
6.1%
l 13051
 
5.0%
d 13047
 
5.0%
s 12632
 
4.9%
i 10710
 
4.1%
Other values (15) 80306
31.0%

puertas
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size629.8 KiB
5
27235 
4
10673 
2
 
1982
3
 
419

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters40309
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row4
4th row4
5th row5

Common Values

ValueCountFrequency (%)
5 27235
67.6%
4 10673
 
26.5%
2 1982
 
4.9%
3 419
 
1.0%

Length

2024-05-20T00:23:43.351973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:23:43.432236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 27235
67.6%
4 10673
 
26.5%
2 1982
 
4.9%
3 419
 
1.0%

Most occurring characters

ValueCountFrequency (%)
5 27235
67.6%
4 10673
 
26.5%
2 1982
 
4.9%
3 419
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 27235
67.6%
4 10673
 
26.5%
2 1982
 
4.9%
3 419
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 27235
67.6%
4 10673
 
26.5%
2 1982
 
4.9%
3 419
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 27235
67.6%
4 10673
 
26.5%
2 1982
 
4.9%
3 419
 
1.0%

motor
Real number (ℝ)

MISSING 

Distinct45
Distinct (%)0.1%
Missing3008
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean2.0573309
Minimum0
Maximum5.7
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:43.554667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2
Q11.6
median2
Q32.4
95-th percentile3.6
Maximum5.7
Range5.7
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.73446097
Coefficient of variation (CV)0.35699701
Kurtosis2.4408306
Mean2.0573309
Median Absolute Deviation (MAD)0.4
Skewness1.3782378
Sum76740.5
Variance0.53943292
MonotonicityNot monotonic
2024-05-20T00:23:43.932039image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
2 9019
22.4%
1.6 8020
19.9%
2.5 2655
 
6.6%
1.8 2045
 
5.1%
1.4 2002
 
5.0%
3 1852
 
4.6%
2.4 1775
 
4.4%
1.2 1769
 
4.4%
1.5 1596
 
4.0%
1 1197
 
3.0%
Other values (35) 5371
13.3%
(Missing) 3008
 
7.5%
ValueCountFrequency (%)
0 19
 
< 0.1%
0.8 59
 
0.1%
0.9 1
 
< 0.1%
1 1197
 
3.0%
1.1 55
 
0.1%
1.2 1769
 
4.4%
1.3 375
 
0.9%
1.4 2002
 
5.0%
1.5 1596
 
4.0%
1.6 8020
19.9%
ValueCountFrequency (%)
5.7 59
0.1%
5.6 6
 
< 0.1%
5.5 18
 
< 0.1%
5.4 5
 
< 0.1%
5.3 39
 
0.1%
5.2 3
 
< 0.1%
5 48
0.1%
4.7 57
0.1%
4.6 26
 
0.1%
4.5 100
0.2%

tipo_de_combustible
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size354.5 KiB
gasolina
35354 
diesel
 
3300
hibrido
 
1351
gasolina y gas
 
203
electrico
 
101

Length

Max length14
Median length8
Mean length7.835471
Min length6

Characters and Unicode

Total characters315840
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgasolina
2nd rowgasolina
3rd rowgasolina
4th rowgasolina
5th rowgasolina

Common Values

ValueCountFrequency (%)
gasolina 35354
87.7%
diesel 3300
 
8.2%
hibrido 1351
 
3.4%
gasolina y gas 203
 
0.5%
electrico 101
 
0.3%

Length

2024-05-20T00:23:44.057066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:23:44.135437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
gasolina 35557
87.3%
diesel 3300
 
8.1%
hibrido 1351
 
3.3%
y 203
 
0.5%
gas 203
 
0.5%
electrico 101
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 71317
22.6%
i 41660
13.2%
s 39060
12.4%
l 38958
12.3%
o 37009
11.7%
g 35760
11.3%
n 35557
11.3%
e 6802
 
2.2%
d 4651
 
1.5%
r 1452
 
0.5%
Other values (6) 3614
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 315840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 71317
22.6%
i 41660
13.2%
s 39060
12.4%
l 38958
12.3%
o 37009
11.7%
g 35760
11.3%
n 35557
11.3%
e 6802
 
2.2%
d 4651
 
1.5%
r 1452
 
0.5%
Other values (6) 3614
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 315840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 71317
22.6%
i 41660
13.2%
s 39060
12.4%
l 38958
12.3%
o 37009
11.7%
g 35760
11.3%
n 35557
11.3%
e 6802
 
2.2%
d 4651
 
1.5%
r 1452
 
0.5%
Other values (6) 3614
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 315840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 71317
22.6%
i 41660
13.2%
s 39060
12.4%
l 38958
12.3%
o 37009
11.7%
g 35760
11.3%
n 35557
11.3%
e 6802
 
2.2%
d 4651
 
1.5%
r 1452
 
0.5%
Other values (6) 3614
 
1.1%

kilometros
Real number (ℝ)

SKEWED 

Distinct9002
Distinct (%)22.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71182.621
Minimum1
Maximum6350000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:44.234938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8991.6
Q133996
median64000
Q398000
95-th percentile155000
Maximum6350000
Range6349999
Interquartile range (IQR)64004

Descriptive statistics

Standard deviation60046.899
Coefficient of variation (CV)0.84356123
Kurtosis2979.5281
Mean71182.621
Median Absolute Deviation (MAD)32000
Skewness30.088835
Sum2.8693003 × 109
Variance3.6056301 × 109
MonotonicityNot monotonic
2024-05-20T00:23:44.333138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90000 348
 
0.9%
80000 317
 
0.8%
60000 311
 
0.8%
70000 295
 
0.7%
50000 289
 
0.7%
75000 287
 
0.7%
65000 282
 
0.7%
100000 274
 
0.7%
45000 263
 
0.7%
40000 261
 
0.6%
Other values (8992) 37382
92.7%
ValueCountFrequency (%)
1 78
0.2%
2 2
 
< 0.1%
3 5
 
< 0.1%
4 1
 
< 0.1%
5 7
 
< 0.1%
6.2 1
 
< 0.1%
6.4 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
6350000 1
< 0.1%
999999 2
< 0.1%
960020 1
< 0.1%
913000 1
< 0.1%
870000 1
< 0.1%
840666 1
< 0.1%
819000 1
< 0.1%
803802 1
< 0.1%
800000 1
< 0.1%
790000 1
< 0.1%

modelo
Categorical

HIGH CARDINALITY 

Distinct939
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size436.1 KiB
duster
 
1074
3
 
969
prado
 
942
tracker
 
915
fortuner
 
841
Other values (934)
35568 

Length

Max length47
Median length38
Mean length5.9544767
Min length0

Characters and Unicode

Total characters240019
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique447 ?
Unique (%)1.1%

Sample

1st rowcompass
2nd row2
3rd row3
4th rowkangoo
5th rowtucson

Common Values

ValueCountFrequency (%)
duster 1074
 
2.7%
3 969
 
2.4%
prado 942
 
2.3%
tracker 915
 
2.3%
fortuner 841
 
2.1%
escape 827
 
2.1%
cx_5 819
 
2.0%
logan 817
 
2.0%
sportage 817
 
2.0%
sandero 762
 
1.9%
Other values (929) 31526
78.2%

Length

2024-05-20T00:23:44.470119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
clase 1857
 
4.0%
3 1392
 
3.0%
duster 1221
 
2.6%
2 970
 
2.1%
prado 966
 
2.1%
tracker 915
 
2.0%
sportage 876
 
1.9%
sandero 863
 
1.8%
fortuner 851
 
1.8%
escape 829
 
1.8%
Other values (745) 36139
77.1%

Most occurring characters

ValueCountFrequency (%)
a 23802
 
9.9%
r 22839
 
9.5%
e 22435
 
9.3%
o 17840
 
7.4%
s 15984
 
6.7%
t 14900
 
6.2%
c 13842
 
5.8%
i 11227
 
4.7%
n 10979
 
4.6%
l 9563
 
4.0%
Other values (31) 76608
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 240019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 23802
 
9.9%
r 22839
 
9.5%
e 22435
 
9.3%
o 17840
 
7.4%
s 15984
 
6.7%
t 14900
 
6.2%
c 13842
 
5.8%
i 11227
 
4.7%
n 10979
 
4.6%
l 9563
 
4.0%
Other values (31) 76608
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 240019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 23802
 
9.9%
r 22839
 
9.5%
e 22435
 
9.3%
o 17840
 
7.4%
s 15984
 
6.7%
t 14900
 
6.2%
c 13842
 
5.8%
i 11227
 
4.7%
n 10979
 
4.6%
l 9563
 
4.0%
Other values (31) 76608
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 240019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 23802
 
9.9%
r 22839
 
9.5%
e 22435
 
9.3%
o 17840
 
7.4%
s 15984
 
6.7%
t 14900
 
6.2%
c 13842
 
5.8%
i 11227
 
4.7%
n 10979
 
4.6%
l 9563
 
4.0%
Other values (31) 76608
31.9%
Distinct9308
Distinct (%)23.1%
Missing2
Missing (%)< 0.1%
Memory size629.8 KiB
2024-05-20T00:23:44.982050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length84
Median length62
Mean length13.218746
Min length1

Characters and Unicode

Total characters532808
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7022 ?
Unique (%)17.4%

Sample

1st row2.4 Limited
2nd row1.5
3rd row2.0 Lxhm7
4th row1.6 Express 4 p
5th rowGl
ValueCountFrequency (%)
2.0 7526
 
6.9%
1.6 6669
 
6.1%
touring 2854
 
2.6%
at 2381
 
2.2%
2.5 2168
 
2.0%
4x4 2130
 
1.9%
4x2 1996
 
1.8%
1.8 1873
 
1.7%
1.4 1823
 
1.7%
grand 1659
 
1.5%
Other values (2375) 78686
71.7%
2024-05-20T00:23:45.615264image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
69675
 
13.1%
. 34001
 
6.4%
i 28560
 
5.4%
e 23124
 
4.3%
2 22515
 
4.2%
t 20391
 
3.8%
n 19849
 
3.7%
1 18655
 
3.5%
0 18055
 
3.4%
r 16953
 
3.2%
Other values (88) 261030
49.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
69675
 
13.1%
. 34001
 
6.4%
i 28560
 
5.4%
e 23124
 
4.3%
2 22515
 
4.2%
t 20391
 
3.8%
n 19849
 
3.7%
1 18655
 
3.5%
0 18055
 
3.4%
r 16953
 
3.2%
Other values (88) 261030
49.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
69675
 
13.1%
. 34001
 
6.4%
i 28560
 
5.4%
e 23124
 
4.3%
2 22515
 
4.2%
t 20391
 
3.8%
n 19849
 
3.7%
1 18655
 
3.5%
0 18055
 
3.4%
r 16953
 
3.2%
Other values (88) 261030
49.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
69675
 
13.1%
. 34001
 
6.4%
i 28560
 
5.4%
e 23124
 
4.3%
2 22515
 
4.2%
t 20391
 
3.8%
n 19849
 
3.7%
1 18655
 
3.5%
0 18055
 
3.4%
r 16953
 
3.2%
Other values (88) 261030
49.0%

año
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.4547
Minimum2010
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:45.715739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2011
Q12014
median2018
Q32021
95-th percentile2023
Maximum2024
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7927821
Coefficient of variation (CV)0.0018799838
Kurtosis-1.0305281
Mean2017.4547
Median Absolute Deviation (MAD)3
Skewness-0.16536969
Sum81321581
Variance14.385196
MonotonicityIncreasing
2024-05-20T00:23:45.799162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2020 3652
9.1%
2022 3624
9.0%
2019 3554
8.8%
2017 3483
8.6%
2023 3308
 
8.2%
2018 3217
 
8.0%
2015 3071
 
7.6%
2016 2863
 
7.1%
2014 2720
 
6.7%
2021 2537
 
6.3%
Other values (5) 8280
20.5%
ValueCountFrequency (%)
2010 984
 
2.4%
2011 1960
4.9%
2012 2129
5.3%
2013 2460
6.1%
2014 2720
6.7%
2015 3071
7.6%
2016 2863
7.1%
2017 3483
8.6%
2018 3217
8.0%
2019 3554
8.8%
ValueCountFrequency (%)
2024 747
 
1.9%
2023 3308
8.2%
2022 3624
9.0%
2021 2537
6.3%
2020 3652
9.1%
2019 3554
8.8%
2018 3217
8.0%
2017 3483
8.6%
2016 2863
7.1%
2015 3071
7.6%

cilindrada
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)0.1%
Missing8992
Missing (%)22.3%
Infinite0
Infinite (%)0.0%
Mean2.0475205
Minimum-1
Maximum5.7
Zeros3
Zeros (%)< 0.1%
Negative15
Negative (%)< 0.1%
Memory size629.8 KiB
2024-05-20T00:23:45.915027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.2
Q11.6
median2
Q32.4
95-th percentile3.5
Maximum5.7
Range6.7
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.72012208
Coefficient of variation (CV)0.35170445
Kurtosis2.6567473
Mean2.0475205
Median Absolute Deviation (MAD)0.4
Skewness1.3613242
Sum64122.2
Variance0.51857581
MonotonicityNot monotonic
2024-05-20T00:23:46.031461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2 7737
19.2%
1.6 7033
17.4%
2.5 2176
 
5.4%
1.8 1831
 
4.5%
1.4 1704
 
4.2%
2.4 1487
 
3.7%
3 1486
 
3.7%
1.2 1455
 
3.6%
1.5 1123
 
2.8%
1 951
 
2.4%
Other values (32) 4334
10.8%
(Missing) 8992
22.3%
ValueCountFrequency (%)
-1 15
 
< 0.1%
0 3
 
< 0.1%
0.8 59
 
0.1%
1 951
 
2.4%
1.1 47
 
0.1%
1.2 1455
 
3.6%
1.3 251
 
0.6%
1.4 1704
 
4.2%
1.5 1123
 
2.8%
1.6 7033
17.4%
ValueCountFrequency (%)
5.7 52
0.1%
5.6 2
 
< 0.1%
5.5 22
 
0.1%
5.4 2
 
< 0.1%
5.3 16
 
< 0.1%
5.2 3
 
< 0.1%
5 25
 
0.1%
4.7 58
0.1%
4.6 13
 
< 0.1%
4.5 69
0.2%

capacidad_de_personas
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4026148
Minimum-1
Maximum8
Zeros0
Zeros (%)0.0%
Negative9622
Negative (%)23.9%
Memory size472.4 KiB
2024-05-20T00:23:46.131298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q12
median5
Q35
95-th percentile5
Maximum8
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5586063
Coefficient of variation (CV)0.75195298
Kurtosis-0.79110362
Mean3.4026148
Median Absolute Deviation (MAD)0
Skewness-1.051225
Sum137156
Variance6.5464663
MonotonicityNot monotonic
2024-05-20T00:23:46.214854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 28410
70.5%
-1 9622
 
23.9%
2 2242
 
5.6%
7 28
 
0.1%
8 5
 
< 0.1%
4 2
 
< 0.1%
ValueCountFrequency (%)
-1 9622
 
23.9%
2 2242
 
5.6%
4 2
 
< 0.1%
5 28410
70.5%
7 28
 
0.1%
8 5
 
< 0.1%
ValueCountFrequency (%)
8 5
 
< 0.1%
7 28
 
0.1%
5 28410
70.5%
4 2
 
< 0.1%
2 2242
 
5.6%
-1 9622
 
23.9%

potencia
Real number (ℝ)

MISSING 

Distinct235
Distinct (%)0.7%
Missing8735
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean158.48809
Minimum10
Maximum585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:46.317541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile81
Q1109
median148
Q3184
95-th percentile285
Maximum585
Range575
Interquartile range (IQR)75

Descriptive statistics

Standard deviation68.700887
Coefficient of variation (CV)0.43347665
Kurtosis3.3126316
Mean158.48809
Median Absolute Deviation (MAD)37
Skewness1.4673777
Sum5004103
Variance4719.8118
MonotonicityNot monotonic
2024-05-20T00:23:46.454624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138 1405
 
3.5%
153 1343
 
3.3%
106 1219
 
3.0%
170 1202
 
3.0%
85 910
 
2.3%
99 776
 
1.9%
120 770
 
1.9%
115 733
 
1.8%
143 711
 
1.8%
105 710
 
1.8%
Other values (225) 21795
54.1%
(Missing) 8735
21.7%
ValueCountFrequency (%)
10 46
 
0.1%
11 72
0.2%
12 37
 
0.1%
13 26
 
0.1%
47 43
 
0.1%
55 25
 
0.1%
56 1
 
< 0.1%
62 95
0.2%
64 9
 
< 0.1%
65 156
0.4%
ValueCountFrequency (%)
585 1
 
< 0.1%
567 13
< 0.1%
557 2
 
< 0.1%
555 1
 
< 0.1%
550 14
< 0.1%
536 1
 
< 0.1%
525 3
 
< 0.1%
523 2
 
< 0.1%
503 1
 
< 0.1%
497 1
 
< 0.1%

control_de_traccion
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing3433
Missing (%)8.5%
Memory size354.5 KiB
delantera
14800 
4x4
11308 
4x2
9794 
trasera
 
974

Length

Max length9
Median length3
Mean length5.5137217
Min length3

Characters and Unicode

Total characters203324
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4x4
2nd rowdelantera
3rd rowdelantera
4th row4x2
5th rowdelantera

Common Values

ValueCountFrequency (%)
delantera 14800
36.7%
4x4 11308
28.1%
4x2 9794
24.3%
trasera 974
 
2.4%
(Missing) 3433
 
8.5%

Length

2024-05-20T00:23:46.600605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:23:46.714610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
delantera 14800
40.1%
4x4 11308
30.7%
4x2 9794
26.6%
trasera 974
 
2.6%

Most occurring characters

ValueCountFrequency (%)
4 32410
15.9%
a 31548
15.5%
e 30574
15.0%
x 21102
10.4%
r 16748
8.2%
t 15774
7.8%
d 14800
7.3%
l 14800
7.3%
n 14800
7.3%
2 9794
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 32410
15.9%
a 31548
15.5%
e 30574
15.0%
x 21102
10.4%
r 16748
8.2%
t 15774
7.8%
d 14800
7.3%
l 14800
7.3%
n 14800
7.3%
2 9794
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 32410
15.9%
a 31548
15.5%
e 30574
15.0%
x 21102
10.4%
r 16748
8.2%
t 15774
7.8%
d 14800
7.3%
l 14800
7.3%
n 14800
7.3%
2 9794
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 32410
15.9%
a 31548
15.5%
e 30574
15.0%
x 21102
10.4%
r 16748
8.2%
t 15774
7.8%
d 14800
7.3%
l 14800
7.3%
n 14800
7.3%
2 9794
 
4.8%

transmision
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size629.8 KiB
automatica
22061 
mecanica
18248 

Length

Max length10
Median length10
Mean length9.0945943
Min length8

Characters and Unicode

Total characters366594
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmecanica
2nd rowautomatica
3rd rowautomatica
4th rowmecanica
5th rowmecanica

Common Values

ValueCountFrequency (%)
automatica 22061
54.7%
mecanica 18248
45.3%

Length

2024-05-20T00:23:46.831368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-20T00:23:46.931153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
automatica 22061
54.7%
mecanica 18248
45.3%

Most occurring characters

ValueCountFrequency (%)
a 102679
28.0%
c 58557
16.0%
t 44122
12.0%
m 40309
 
11.0%
i 40309
 
11.0%
u 22061
 
6.0%
o 22061
 
6.0%
e 18248
 
5.0%
n 18248
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 102679
28.0%
c 58557
16.0%
t 44122
12.0%
m 40309
 
11.0%
i 40309
 
11.0%
u 22061
 
6.0%
o 22061
 
6.0%
e 18248
 
5.0%
n 18248
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 102679
28.0%
c 58557
16.0%
t 44122
12.0%
m 40309
 
11.0%
i 40309
 
11.0%
u 22061
 
6.0%
o 22061
 
6.0%
e 18248
 
5.0%
n 18248
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 102679
28.0%
c 58557
16.0%
t 44122
12.0%
m 40309
 
11.0%
i 40309
 
11.0%
u 22061
 
6.0%
o 22061
 
6.0%
e 18248
 
5.0%
n 18248
 
5.0%

age
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5453125
Minimum0
Maximum14
Zeros747
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size629.8 KiB
2024-05-20T00:23:47.036878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q310
95-th percentile13
Maximum14
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7927821
Coefficient of variation (CV)0.5794654
Kurtosis-1.0305281
Mean6.5453125
Median Absolute Deviation (MAD)3
Skewness0.16536969
Sum263835
Variance14.385196
MonotonicityDecreasing
2024-05-20T00:23:47.130287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 3652
9.1%
2 3624
9.0%
5 3554
8.8%
7 3483
8.6%
1 3308
 
8.2%
6 3217
 
8.0%
9 3071
 
7.6%
8 2863
 
7.1%
10 2720
 
6.7%
3 2537
 
6.3%
Other values (5) 8280
20.5%
ValueCountFrequency (%)
0 747
 
1.9%
1 3308
8.2%
2 3624
9.0%
3 2537
6.3%
4 3652
9.1%
5 3554
8.8%
6 3217
8.0%
7 3483
8.6%
8 2863
7.1%
9 3071
7.6%
ValueCountFrequency (%)
14 984
 
2.4%
13 1960
4.9%
12 2129
5.3%
11 2460
6.1%
10 2720
6.7%
9 3071
7.6%
8 2863
7.1%
7 3483
8.6%
6 3217
8.0%
5 3554
8.8%

Interactions

2024-05-20T00:23:36.606251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.290791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.019612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.689849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.455404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.501896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.260751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.938577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.688269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.370003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.090921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.772606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.552338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.606499image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.351286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.034747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.771328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.481277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.178385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.857296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.638332image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.706250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.436421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.105984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.870523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.572485image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.252790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.953277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.743108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.802063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.518111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.204691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.967869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.677260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.352884image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.053601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.838237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.886161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.618085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.287883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:37.054000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.757612image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.436687image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.163527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.952578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.985232image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.707276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.370998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:37.117788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.841095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.519167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.263003image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.269991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.082929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.787036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.438626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:37.217442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:31.922103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:32.608827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:33.352280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:34.388162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.155904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:35.857220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-20T00:23:36.521547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-20T00:23:37.386844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-20T00:23:37.719836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-20T00:23:38.101187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_cartitlelinkpriceseller_namecitycoloraire_acondicionadomarcapuertasmotortipo_de_combustiblekilometrosmodeloversionañocilindradacapacidad_de_personaspotenciacontrol_de_tracciontransmisionage
0MCO2391186016Jeep Compass 2.4 Limitedhttps://carro.mercadolibre.com.co/MCO-2391186016-jeep-compass-24-limited-_JM37990000TUC_LAPALMERACHIAbogota dcnegrosijeep52.4gasolina182000.0compass2.4 Limited20102.45172.04x4mecanica14
1MCO2391197332Mazda 2 1.5 2010https://carro.mercadolibre.com.co/MCO-2391197332-mazda-2-15-2010-_JM32000000ZHERRERA84044bogota dcblancosimazda51.5gasolina146330.021.52010NaN-1NaNNaNautomatica14
2MCO1426750375Mazda 3 Lxha7 At 2.0 2010https://carro.mercadolibre.com.co/MCO-1426750375-mazda-3-lxha7-at-20-2010-_JM30500000OGARZON81312bogota dcplateadosimazda42.0gasolina106000.032.0 Lxhm720102.05145.0delanteraautomatica14
3MCO1426711773Renault Kangoo F76 1.6 2010https://carro.mercadolibre.com.co/MCO-1426711773-renault-kangoo-f76-16-2010-_JM24500000JARROYAVE29599bogota dcblancosirenault41.6gasolina170500.0kangoo1.6 Express 4 p20101.6295.0delanteramecanica14
4MCO1425593285Hyundai Tucsonhttps://carro.mercadolibre.com.co/MCO-1425593285-hyundai-tucson-_JM43000000TUC_SUBAbogota dcplateadosihyundai52.0gasolina110050.0tucsonGl2010NaN-1NaN4x2mecanica14
5MCO2387326852Renault Stepway 1.6lhttps://carro.mercadolibre.com.co/MCO-2387326852-renault-stepway-16l-_JM27000000ABARRERO21250bogota dcrojosirenault51.6gasolina94783.0stepway1.6l20101.65110.0delanteramecanica14
6MCO1424639637Chevrolet Spark Lthttps://carro.mercadolibre.com.co/MCO-1424639637-chevrolet-spark-lt-_JM21500000TUC_CALLE134bogota dcgrisnochevrolet51.0gasolina92000.0spark1.0 Lt20101.0562.04x2mecanica14
7MCO1423454057Chevrolet Aveo Emotion 1.6 Gt 2010https://carro.mercadolibre.com.co/MCO-1423454057-chevrolet-aveo-emotion-16-gt-2010-_JM25800000JLEON86105bogota dcplateadosichevrolet51.6gasolina146000.0aveo emotion1.6 Gt2010NaN-1NaNNaNmecanica14
8MCO2375421964Kia Sportagehttps://carro.mercadolibre.com.co/MCO-2375421964-kia-sportage-_JM36500000TUC_KENNEDYbogota dcplateadosikia52.0gasolina125403.0sportage2.0 Lx 4x22010NaN-1NaNNaNmecanica14
9MCO1422666347Honda Civic Lx 1.8https://carro.mercadolibre.com.co/MCO-1422666347-honda-civic-lx-18-_JM36000000JMARTINEZ83902bogota dcblancosihonda41.8gasolina116000.0civic1.8 Lx20101.85140.0delanteramecanica14
id_cartitlelinkpriceseller_namecitycoloraire_acondicionadomarcapuertasmotortipo_de_combustiblekilometrosmodeloversionañocilindradacapacidad_de_personaspotenciacontrol_de_tracciontransmisionage
43337MCO1419381819Renault Sandero 2024 1.6 Life Plushttps://carro.mercadolibre.com.co/MCO-1419381819-renault-sandero-2024-16-life-plus-_JM64090000SANTIAGOOSPINA610antioquiaotronorenault51.6gasolina1.0sandero1.6 Life Plus2024NaN-1113.0delanteramecanica0
43338MCO1420833527Cheviplanhttps://carro.mercadolibre.com.co/MCO-1420833527-cheviplan-_JM3750000ANDORESantioquiablanconochevrolet41.4gasolina5000.0n_a242024NaN-1NaNdelanteraautomatica0
43339MCO2391833712Renault Duster 2024 1.3 T Iconic Mt 4x4https://carro.mercadolibre.com.co/MCO-2391833712-renault-duster-2024-13-t-iconic-mt-4x4-_JM94000000CARLOSFERNEYMOYAMARTNantioquiaotrosirenault51.3gasolina5718.0duster1.3 T Iconic Mt 4X420241.35154.04x4mecanica0
43340MCO1423756669Mazda 3 Touringhttps://carro.mercadolibre.com.co/MCO-1423756669-mazda-3-touring-_JM112000000GOSE5905441valle del caucaotronomazda5NaNhibrido14000.03Touring2024NaN-1NaNNaNmecanica0
43341MCO2358833262Bmw X3 Idrive30i 2023https://carro.mercadolibre.com.co/MCO-2358833262-bmw-x3-idrive30i-2023-_JM235000000LORU20240503163322bogota dcazulsibmw52.0gasolina6300.0x32.0 Xdrive30i20242.05252.04x4automatica0
43342MCO1419580825Mazda Cx-5 2024 2.0 Touring 4x2 Authttps://carro.mercadolibre.com.co/MCO-1419580825-mazda-cx-5-2024-20-touring-4x2-aut-_JM120000000JIMENEZVICTOR20220202210039caldasotronomazda52.0gasolina14000.0cx_52.0 Touring 4X2 Aut2024NaN-1154.04x2automatica0
43343MCO1420286429Mazda Cx-5 2024 2.5 Touring 4x2 Authttps://carro.mercadolibre.com.co/MCO-1420286429-mazda-cx-5-2024-25-touring-4x2-aut-_JM135000000ZEDI6261157valle del caucaotronomazda52.5gasolina19000.0cx_52.5 Touring 4X2 Aut2024NaN-1188.04x2automatica0
43344MCO2347166584Toyota Corolla Cross Xei Hybridhttps://carro.mercadolibre.com.co/MCO-2347166584-toyota-corolla-cross-xei-hybrid-_JM131000000TUC_CALLE134cundinamarcagrissitoyota51.8hibrido15600.0corolla cross1.8 Xei Hybrid2024NaN-197.04x2automatica0
43345MCO1415231981Renault Duster Iconic 1.3 Turbo 4x4https://carro.mercadolibre.com.co/MCO-1415231981-renault-duster-iconic-13-turbo-4x4-_JM98000000ACUEVAS33614bogota dcgrissirenault51.3gasolina1483.0dusterICONIC 1.3 TURBO 4X42024NaN-1154.04x4mecanica0
43347MCO2335060240Toyota Hilux 2024 2.8 Gr-shttps://carro.mercadolibre.com.co/MCO-2335060240-toyota-hilux-2024-28-gr-s-_JM312000000RAUTOSGROUPbogota dcblanconotoyota42.8diesel200.0hilux2.8 Gr-S2024NaN-1201.04x4automatica0